Diabetes Network Pharmacology Dataset
Abstract
The Real-World Diabetes Network Pharmacology Dataset (RDNPD 2025) is a large-scale multimodal healthcare dataset developed for diabetes severity prediction, biomedical graph-learning analysis, and federated healthcare intelligence research. The dataset contains 466,605 patient records collected from five distributed healthcare institutions, including tertiary hospitals, diabetes treatment centers, and remote clinical facilities. The dataset was designed to support research on wearable-assisted healthcare monitoring, explainable artificial intelligence, biomedical interaction modeling, and privacy-preserving federated learning systems. The dataset integrates heterogeneous healthcare information obtained from wearable physiological sensing devices, electronic health records, laboratory investigations, medication histories, and biomedical network pharmacology resources. The included demographic and lifestyle attributes consist of age, gender, body mass index, waist circumference, smoking status, and physical activity level. Physiological and diabetes-related biomarkers include HbA1c, fasting glucose, postprandial glucose, insulin level, HOMA-IR, C-peptide, glycemic variability, blood pressure measurements, heart rate, cholesterol indicators, triglycerides, and cardiovascular risk measurements. The dataset further incorporates organ-damage and complication-related indicators including eGFR, creatinine, albuminuria, liver enzyme measurements, neuropathy score, retinopathy score,…
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Evidence Hub reads this as a dataset, done in computer models (in silico), not in people. Its evidence level is 5 of 5 (Very low · case / narrative / animal / in vitro / in silico). Case reports, narrative reviews, and animal, cell or computer-model studies give the weakest evidence for human health.
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